👋 Welcome Back, Vibe Coders! In [Part 1], we built the “Hard Memory” folder structure to give our AI a brain. Now, we need to give it eyes and hands. Most beginners tell their AI: “Make me a dashboard.” And the AI responds with a React app that auto-refreshes every second. Result? Your IP gets … Read more
Let’s be honest: Most retail trading bots fail. Why? Because they are built on “surface-level” logic found in basic blog posts: “Buy when RSI is under 30” or “Buy when price hits the lower Bollinger Band.” In the world of High-Frequency Trading (HFT), that is just liquidity for the sharks. I wanted to change that. … Read more
Hello, everyone! 👋 In our previous posts, we discussed how to design powerful trading strategies like the “US Closing Price Betting System” using Google’s Gemini and NotebookLM. A common question I get is: “How do I feed the AI better data to make it smarter?” The answer is simple: Quality Input = Quality Output. To … Read more
Have you tried the new “Gems” feature in Google Gemini yet? If you are a developer or a trader, this is an absolute game-changer. For a long time, I’ve wanted to build a Python-based automated trading system based on Mark Minervini’s VCP (Volatility Contraction Pattern) strategy. But translating visual chart patterns into rigorous mathematical code … Read more
This algorithm is purpose-built for the heartbeat of global capital: The US Stock Market. The US market is a unique beast. There are no daily price limits, and it is dominated by High-Frequency Trading (HFT) and institutional algorithms. To survive and thrive here, a system must apply strict standards regarding Relative Strength (RS), Smart Money … Read more
Hello everyone! 👋 Today, I want to share a little story about my experience with Gemini’s “New Gems” feature, which completely surprised me. I just learned how to use it today, and honestly? It made me feel like my Google AI Premium subscription fee is not a waste at all. (Just to be clear—I am … Read more